Super-resolution reconstruction algorithms perform a fusion of several low quality images of the same scene into a single improved quality image. As opposed to this STATIC recovery problem, in this paper we deene a DYNAMIC super-resolution task: the restoration of a blurred, decimated, and noisy image sequence. We rst model this problem through state-space equations, showing that this problem c...

Over the past decade hyper spectral (HS) image analysis has turned into one of the most powerful and growing technologies in the field of remote sensing. While HS images cover large area at fine spectral resolution, their spatial resolutions are often too coarse for the use in various applications. Hence improving their resolution has a high payoff. This paper presents a novel approach for supe...

Principal Component Analysis (PCA) is a classical method which is commonly used for human face images representation in face super-resolution. But the features extracted by PCA are holistic and difficult to have semantic interpretation. In order to synthesize a high-resolution face image with structural details, we propose a face super-resolution algorithm based on non-negative matrix factoriza...

Super resolution (SR) images play an important role in Image processing applications. Spatial resolution is the key parameter in many applications of image processing. Super resolution images can be used to improve the spatial resolution. In this paper a new SR image reconstruction algorithm is proposed using Integer wavelet transform (IWT) and Binary plane technique (BPT). The proposed method ...

As the resolution of output device increases, the demand of high resolution contents has become more eagerly. Therefore, the image superresolution algorithms become more important. In digital image, the edges in the image are related to human perception heavily. Because of this, most recent research topics tend to enhance the image edges to achieve better visual quality. In this paper, we propo...

The spatial resolution of a hyperspectral image is often coarse because of the limitations of the imaging hardware. Super-resolution reconstruction (SRR) is a promising signal post-processing technique for hyperspectral image resolution enhancement. This paper proposes a maximum a posteriori (MAP) based multi-frame super-resolution algorithm for hyperspectral images. Principal component analysi...

The task of providing super-resolution is a task, which is mainly formulated in the inverse form and is solved by the method or a set of methods for preserving the finest details of an image by processing one input image or a set of input images of one scene. The image super-resolution is provided due to an increase in the number pixels per unit area in the original sample. Similar methods for ...

Super-resolution is an important goal of many image acquisition systems. Here we demonstrate the possibility of achieving super-resolution with a single exposure by combining the well known optical scheme of double random phase encoding which has been traditionally used for encryption with results from the relatively new and emerging field of compressive sensing. It is shown that the proposed m...

This paper presents an edge-enhancing super-resolution algorithm using anisotropic diffusion technique. Because we solve the super-resolution problem by incorporating anisotropic diffusion, our technique does more than merely reconstruct a high-resolution image from several overlapping noisy lowresolution images and preserve them. In addition to reducing image noise during the restoration proce...

A super-resolution restoration algorithm based on sample learning is to implement the “learning” between the images with high resolution and low resolution usually in the way of “search and paste”. To improve the quality of restored image by building a huge sample database. The mismatching in search results may lead lower reconstructed quality. To solve these questions, this paper proposes a fa...